Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10127
Missing cells2268
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric15
Categorical6

Alerts

Avg_Open_To_Buy is highly overall correlated with Avg_Utilization_Ratio and 1 other fieldsHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Avg_Open_To_Buy and 1 other fieldsHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Card_Category is highly imbalanced (79.2%)Imbalance
Education_Level has 1519 (15.0%) missing valuesMissing
Marital_Status has 749 (7.4%) missing valuesMissing
CLIENTNUM has unique valuesUnique
Dependent_count has 904 (8.9%) zerosZeros
Contacts_Count_12_mon has 399 (3.9%) zerosZeros
Total_Revolving_Bal has 2470 (24.4%) zerosZeros
Avg_Utilization_Ratio has 2470 (24.4%) zerosZeros

Reproduction

Analysis started2024-09-02 00:24:40.919599
Analysis finished2024-09-02 00:26:07.444591
Duration1 minute and 26.52 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:07.711467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2024-09-02T00:26:08.238155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Existing Customer
8500 
Attrited Customer
1627 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters172159
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowExisting Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 8500
83.9%
Attrited Customer 1627
 
16.1%

Length

2024-09-02T00:26:08.639364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:09.089250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 10127
50.0%
existing 8500
42.0%
attrited 1627
 
8.0%

Most occurring characters

ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:09.426332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2024-09-02T00:26:09.896646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2024-09-02T00:26:10.350302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:10.788424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:11.085596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2024-09-02T00:26:11.501305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Education_Level
Categorical

MISSING 

Distinct6
Distinct (%)0.1%
Missing1519
Missing (%)15.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Uneducated
1487 
College
1013 
Post-Graduate
516 

Length

Max length13
Median length11
Mean length9.2814823
Min length7

Characters and Unicode

Total characters79895
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate 3128
30.9%
High School 2013
19.9%
Uneducated 1487
14.7%
College 1013
 
10.0%
Post-Graduate 516
 
5.1%
Doctorate 451
 
4.5%
(Missing) 1519
15.0%

Length

2024-09-02T00:26:11.977520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:12.457060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
graduate 3128
29.5%
high 2013
19.0%
school 2013
19.0%
uneducated 1487
14.0%
college 1013
 
9.5%
post-graduate 516
 
4.9%
doctorate 451
 
4.2%

Most occurring characters

ValueCountFrequency (%)
a 9226
11.5%
e 9095
11.4%
d 6618
 
8.3%
t 6549
 
8.2%
o 6457
 
8.1%
u 5131
 
6.4%
r 4095
 
5.1%
l 4039
 
5.1%
h 4026
 
5.0%
c 3951
 
4.9%
Other values (13) 20708
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9226
11.5%
e 9095
11.4%
d 6618
 
8.3%
t 6549
 
8.2%
o 6457
 
8.1%
u 5131
 
6.4%
r 4095
 
5.1%
l 4039
 
5.1%
h 4026
 
5.0%
c 3951
 
4.9%
Other values (13) 20708
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9226
11.5%
e 9095
11.4%
d 6618
 
8.3%
t 6549
 
8.2%
o 6457
 
8.1%
u 5131
 
6.4%
r 4095
 
5.1%
l 4039
 
5.1%
h 4026
 
5.0%
c 3951
 
4.9%
Other values (13) 20708
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9226
11.5%
e 9095
11.4%
d 6618
 
8.3%
t 6549
 
8.2%
o 6457
 
8.1%
u 5131
 
6.4%
r 4095
 
5.1%
l 4039
 
5.1%
h 4026
 
5.0%
c 3951
 
4.9%
Other values (13) 20708
25.9%

Marital_Status
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing749
Missing (%)7.4%
Memory size79.2 KiB
Married
4687 
Single
3943 
Divorced
748 

Length

Max length8
Median length7
Mean length6.659309
Min length6

Characters and Unicode

Total characters62451
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 4687
46.3%
Single 3943
38.9%
Divorced 748
 
7.4%
(Missing) 749
 
7.4%

Length

2024-09-02T00:26:12.735078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:13.009808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 4687
50.0%
single 3943
42.0%
divorced 748
 
8.0%

Most occurring characters

ValueCountFrequency (%)
r 10122
16.2%
i 9378
15.0%
e 9378
15.0%
d 5435
8.7%
M 4687
7.5%
a 4687
7.5%
S 3943
 
6.3%
n 3943
 
6.3%
g 3943
 
6.3%
l 3943
 
6.3%
Other values (4) 2992
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10122
16.2%
i 9378
15.0%
e 9378
15.0%
d 5435
8.7%
M 4687
7.5%
a 4687
7.5%
S 3943
 
6.3%
n 3943
 
6.3%
g 3943
 
6.3%
l 3943
 
6.3%
Other values (4) 2992
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10122
16.2%
i 9378
15.0%
e 9378
15.0%
d 5435
8.7%
M 4687
7.5%
a 4687
7.5%
S 3943
 
6.3%
n 3943
 
6.3%
g 3943
 
6.3%
l 3943
 
6.3%
Other values (4) 2992
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10122
16.2%
i 9378
15.0%
e 9378
15.0%
d 5435
8.7%
M 4687
7.5%
a 4687
7.5%
S 3943
 
6.3%
n 3943
 
6.3%
g 3943
 
6.3%
l 3943
 
6.3%
Other values (4) 2992
 
4.8%

Income_Category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
abc
1112 

Length

Max length14
Median length12
Mean length11.040881
Min length3

Characters and Unicode

Total characters111811
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 3561
35.2%
$40K - $60K 1790
17.7%
$80K - $120K 1535
15.2%
$60K - $80K 1402
 
13.8%
abc 1112
 
11.0%
$120K + 727
 
7.2%

Length

2024-09-02T00:26:13.219018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:13.573033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.9%
40k 5351
19.5%
less 3561
13.0%
than 3561
13.0%
60k 3192
11.6%
80k 2937
10.7%
120k 2262
8.2%
abc 1112
 
4.1%

Most occurring characters

ValueCountFrequency (%)
17303
15.5%
0 13742
12.3%
K 13742
12.3%
$ 13742
12.3%
s 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.2%
a 4673
 
4.2%
e 3561
 
3.2%
L 3561
 
3.2%
Other values (10) 24287
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17303
15.5%
0 13742
12.3%
K 13742
12.3%
$ 13742
12.3%
s 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.2%
a 4673
 
4.2%
e 3561
 
3.2%
L 3561
 
3.2%
Other values (10) 24287
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17303
15.5%
0 13742
12.3%
K 13742
12.3%
$ 13742
12.3%
s 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.2%
a 4673
 
4.2%
e 3561
 
3.2%
L 3561
 
3.2%
Other values (10) 24287
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17303
15.5%
0 13742
12.3%
K 13742
12.3%
$ 13742
12.3%
s 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.2%
a 4673
 
4.2%
e 3561
 
3.2%
L 3561
 
3.2%
Other values (10) 24287
21.7%

Card_Category
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9436
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%

Length

2024-09-02T00:26:13.862702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-02T00:26:14.135847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Months_on_book
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:14.367126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2024-09-02T00:26:14.640356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:14.853911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2024-09-02T00:26:15.070828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:15.265982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2024-09-02T00:26:15.465576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:15.672470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2024-09-02T00:26:15.872660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION 

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:16.156206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2024-09-02T00:26:16.445319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:16.740452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2024-09-02T00:26:17.044647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

HIGH CORRELATION 

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:17.315234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2024-09-02T00:26:17.616379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:17.909580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2024-09-02T00:26:18.201412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

HIGH CORRELATION 

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:18.507936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2024-09-02T00:26:18.792686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:19.087322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2024-09-02T00:26:19.401144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71222238
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:19.693117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.23808609
Coefficient of variation (CV)0.33428617
Kurtosis15.689293
Mean0.71222238
Median Absolute Deviation (MAD)0.119
Skewness2.0640306
Sum7212.676
Variance0.056684987
MonotonicityNot monotonic
2024-09-02T00:26:19.976907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3.25 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-09-02T00:26:20.290425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2024-09-02T00:26:20.586592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%

Interactions

2024-09-02T00:26:01.318412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:45.718424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:54.277695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:02.339197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:11.940427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:16.239722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:20.090836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:25.267110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:29.717230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:34.237132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:38.397620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:43.583565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:48.109540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:52.082750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:57.334463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:01.576271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:46.155262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:55.018634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:02.866912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:12.435316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:16.484705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:20.594476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:25.630802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:30.000119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:34.484472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:38.778788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:43.829147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:48.374845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:52.343912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:57.591249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:01.806149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:46.642046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:56.089802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:03.300666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:12.772811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:16.715232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:20.820746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:26.048215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:30.260733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:34.726686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:39.104668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:44.085902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:48.621637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:52.621411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:57.832555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:02.075524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:46.958198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:56.497531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:03.752930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:13.217001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:16.951946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:21.073549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:26.433413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:30.519094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:34.969791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:39.482342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:44.342357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:48.906298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:52.981417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:58.103265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:02.312693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:47.320005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:56.903270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:04.229284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:13.436427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:17.194275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:21.307220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:26.787452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:30.775671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:35.222070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:39.781457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:44.585916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:49.145604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:53.339853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:58.370603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:02.567795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:47.778989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:57.320732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:04.655443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:13.664886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:17.452990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:21.577338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:27.087318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:31.079280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:35.474644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:40.158485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:44.835449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:49.407296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:53.742472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:58.624891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:02.825567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:48.123897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:57.818281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:05.213475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:13.916462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:17.700519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:21.914413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:27.334356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:31.344202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:35.730953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:40.548827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:45.120458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:49.663224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:54.104762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:58.880017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:03.084734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:48.591401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:58.298669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:05.694080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:14.190522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:17.947266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:22.270588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:27.587549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:31.606433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:35.987272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:40.899060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:45.382179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:49.930917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:54.427823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:59.156924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:03.354296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:49.106952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:58.850624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:06.334082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:14.451660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:18.246310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:22.588878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:27.868993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:31.898213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:36.253960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:41.301439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:45.673777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:50.198112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:54.808943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:59.437564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:03.625067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:49.691773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:59.222800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:07.046462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:14.695620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:18.498146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:22.940638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:28.128008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:32.174912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:36.507725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:41.700775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:45.917959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:50.452497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:55.131931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:59.682242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:04.551870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:50.210991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:59.585336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:07.693588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:14.966552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:18.759549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:23.318959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:28.386431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:32.877018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:36.762293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:42.104849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:46.182332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:50.717252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:55.485604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:59.945265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:04.809886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:50.907590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:00.056646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:08.377525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:15.256740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:19.026496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:23.726579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:28.645008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:33.181234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:37.061867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:42.524107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:47.001890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:50.988591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:55.853942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:00.220900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:05.067495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:52.222560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:00.544693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:09.097916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:15.500275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:19.315253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:24.098141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:28.907691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:33.452877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:37.414141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:42.779182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:47.284664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:51.270409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:56.230079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:00.514331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:05.313275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:52.830271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:01.213845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:10.693361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:15.734171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:19.564428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:24.449357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:29.183210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:33.702958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:37.691196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:43.051489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:47.549219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:51.535473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:56.586761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:00.781274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:05.580353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:24:53.696969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:01.667255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:11.404965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:15.982651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:19.836250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:24.843289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:29.459324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:33.976521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:38.019881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:43.317654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:47.835458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:51.822245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:25:56.997542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-02T00:26:01.067375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-02T00:26:20.848174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Attrition_FlagAvg_Open_To_BuyAvg_Utilization_RatioCLIENTNUMCard_CategoryContacts_Count_12_monCredit_LimitCustomer_AgeDependent_countEducation_LevelGenderIncome_CategoryMarital_StatusMonths_Inactive_12_monMonths_on_bookTotal_Amt_Chng_Q4_Q1Total_Ct_Chng_Q4_Q1Total_Relationship_CountTotal_Revolving_BalTotal_Trans_AmtTotal_Trans_Ct
Attrition_Flag1.0000.0190.2410.0480.0000.2390.0320.0240.0210.0280.0360.0280.0190.1960.0190.1840.3140.1660.4020.3250.458
Avg_Open_To_Buy0.0191.000-0.6860.0110.3370.0330.931-0.0020.0540.0010.4400.2780.035-0.0160.0080.007-0.040-0.071-0.1540.0220.022
Avg_Utilization_Ratio0.241-0.6861.0000.0070.149-0.059-0.4170.011-0.0350.0000.2790.1650.034-0.027-0.0040.0330.0940.0650.7090.0190.040
CLIENTNUM0.0480.0110.0071.0000.0000.0110.0140.017-0.0040.0180.0140.0030.000-0.0080.1110.0240.0160.0140.003-0.0020.006
Card_Category0.0000.3370.1490.0001.0000.0100.3350.0210.0180.0090.0840.0530.0340.0000.0130.0240.0000.0670.0190.1540.109
Contacts_Count_12_mon0.2390.033-0.0590.0110.0101.0000.023-0.014-0.0410.0000.0590.0150.0000.030-0.008-0.021-0.0930.061-0.045-0.167-0.168
Credit_Limit0.0320.931-0.4170.0140.3350.0231.0000.0020.0510.0000.4390.2780.032-0.0280.0070.021-0.011-0.0590.1310.0280.034
Customer_Age0.024-0.0020.0110.0170.021-0.0140.0021.000-0.1440.0180.0000.0840.0810.0440.769-0.071-0.040-0.0140.014-0.039-0.054
Dependent_count0.0210.054-0.035-0.0040.018-0.0410.051-0.1441.0000.0000.0000.0430.032-0.009-0.115-0.0260.009-0.036-0.0040.0580.053
Education_Level0.0280.0010.0000.0180.0090.0000.0000.0180.0001.0000.0160.0180.0170.0000.0110.0000.0000.0000.0110.0120.014
Gender0.0360.4400.2790.0140.0840.0590.4390.0000.0000.0161.0000.8390.0060.0190.0110.0440.0500.0000.0330.2470.163
Income_Category0.0280.2780.1650.0030.0530.0150.2780.0840.0430.0180.8391.0000.0060.0170.0460.0150.0230.0070.0220.0930.056
Marital_Status0.0190.0350.0340.0000.0340.0000.0320.0810.0320.0170.0060.0061.0000.0000.0460.0660.0410.0110.0240.1260.123
Months_Inactive_12_mon0.196-0.016-0.027-0.0080.0000.030-0.0280.044-0.0090.0000.0190.0170.0001.0000.057-0.019-0.047-0.007-0.043-0.032-0.051
Months_on_book0.0190.008-0.0040.1110.013-0.0080.0070.769-0.1150.0110.0110.0460.0460.0571.000-0.054-0.034-0.0140.006-0.029-0.039
Total_Amt_Chng_Q4_Q10.1840.0070.0330.0240.024-0.0210.021-0.071-0.0260.0000.0440.0150.066-0.019-0.0541.0000.3020.0260.0360.1350.085
Total_Ct_Chng_Q4_Q10.314-0.0400.0940.0160.000-0.093-0.011-0.0400.0090.0000.0500.0230.041-0.047-0.0340.3021.0000.0240.0780.2230.233
Total_Relationship_Count0.166-0.0710.0650.0140.0670.061-0.059-0.014-0.0360.0000.0000.0070.011-0.007-0.0140.0260.0241.0000.012-0.279-0.227
Total_Revolving_Bal0.402-0.1540.7090.0030.019-0.0450.1310.014-0.0040.0110.0330.0220.024-0.0430.0060.0360.0780.0121.0000.0180.040
Total_Trans_Amt0.3250.0220.019-0.0020.154-0.1670.028-0.0390.0580.0120.2470.0930.126-0.032-0.0290.1350.223-0.2790.0181.0000.880
Total_Trans_Ct0.4580.0220.0400.0060.109-0.1680.034-0.0540.0530.0140.1630.0560.123-0.051-0.0390.0850.233-0.2270.0400.8801.000

Missing values

2024-09-02T00:26:05.973070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-02T00:26:06.719366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-02T00:26:07.170068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
0768805383Existing Customer45M3High SchoolMarried$60K - $80KBlue3951312691.077711914.01.3351144421.6250.061
1818770008Existing Customer49F5GraduateSingleLess than $40KBlue446128256.08647392.01.5411291333.7140.105
2713982108Existing Customer51M3GraduateMarried$80K - $120KBlue364103418.003418.02.5941887202.3330.000
3769911858Existing Customer40F4High SchoolNaNLess than $40KBlue343413313.02517796.01.4051171202.3330.760
4709106358Existing Customer40M3UneducatedMarried$60K - $80KBlue215104716.004716.02.175816282.5000.000
5713061558Existing Customer44M2GraduateMarried$40K - $60KBlue363124010.012472763.01.3761088240.8460.311
6810347208Existing Customer51M4NaNMarried$120K +Gold4661334516.0226432252.01.9751330310.7220.066
7818906208Existing Customer32M0High SchoolNaN$60K - $80KSilver2722229081.0139627685.02.2041538360.7140.048
8710930508Existing Customer37M3UneducatedSingle$60K - $80KBlue3652022352.0251719835.03.3551350241.1820.113
9719661558Existing Customer48M2GraduateSingle$80K - $120KBlue3663311656.016779979.01.5241441320.8820.144
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
10117712503408Existing Customer57M2GraduateMarried$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.106
10118713755458Attrited Customer50M1NaNNaN$80K - $120KBlue366349959.09529007.00.82510310631.1000.096
10119716893683Attrited Customer55F3UneducatedSingleabcBlue4743314657.0251712140.00.1666009530.5140.172
10120710841183Existing Customer54M1High SchoolSingle$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.151
10121713899383Existing Customer56F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.164
10122772366833Existing Customer50M2GraduateSingle$40K - $60KBlue403234003.018512152.00.703154761170.8570.462
10123710638233Attrited Customer41M2NaNDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.511
10124716506083Attrited Customer44F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.000
10125717406983Attrited Customer30M2GraduateNaN$40K - $60KBlue364335281.005281.00.5358395620.7220.000
10126714337233Attrited Customer43F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.189